Local-global dynamic correlations based spatial-temporal convolutional network for traffic flow forecasting  

作　　者：ZHANG Hong GONG Lei ZHAO Tianxin ZHANG Xijun WANG Hongyan 张红(College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,P.R.China)

机构地区：[1]College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,P.R.China

出　　处：《High Technology Letters》2024年第4期370-379,共10页高技术通讯（英文版）

基　　金：Supported by the Key R&D Program of Gansu Province(No.23YFGA0063);the National Natural Science Foundation of China(No.62363022,61663021);the Natural Science Foundation of Gansu Province(No.22JR5RA226,23JRRA886);the Gansu Provincial De-partment of Education:Industrial Support Plan Project(No.2023CYZC-35).

摘　　要：Traffic flow forecasting plays a crucial role and is the key technology to realize dynamic traffic guidance and active traffic control in intelligent traffic systems(ITS).Aiming at the complex local and global spatial-temporal dynamic characteristics of traffic flow,this paper proposes a new traffic flow forecasting model spatial-temporal attention graph neural network(STA-GNN)by combining at-tention mechanism(AM)and spatial-temporal convolutional network.The model learns the hidden dynamic local spatial correlations of the traffic network by combining the dynamic adjacency matrix constructed by the graph learning layer with the graph convolutional network(GCN).The local tem-poral correlations of traffic flow at different scales are extracted by stacking multiple convolutional kernels in temporal convolutional network(TCN).And the global spatial-temporal dependencies of long-time sequences of traffic flow are captured by the spatial-temporal attention mechanism(STAtt),which enhances the global spatial-temporal modeling and the representational ability of model.The experimental results on two datasets,METR-LA and PEMS-BAY,show the proposed STA-GNN model outperforms the common baseline models in forecasting accuracy.

关 键 词：traffic flow forecasting graph convolutional network(GCN) temporal convolu-tional network(TCN) attention mechanism(AM) 

分 类 号：U495[交通运输工程—交通运输规划与管理] TP183[交通运输工程—道路与铁道工程]